metadata
license: other
base_model: meta-llama/Meta-Llama-3-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: Red_Llama_3_base
results: []
See axolotl config
axolotl version: 0.4.0
base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
hub_model_id: KolaGang/Red_Llama_3_base
hub_strategy: end
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: Drewskidang/chatlaw
type: sharegpt
conversation: chatml
- path: Drewskidang/tool
type: sharegpt
conversation: chatml
- path: digitalpipelines/samantha-1.1-uncensored
type: sharegpt
conversation: chatml
- path: KolaGang/mergers
type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./out
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false
wandb_project: Legal_Llama
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 2
micro_batch_size: 4
num_epochs: 3
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
flash_attn_cross_entropy: false
flash_attn_rms_norm: true
flash_attn_fuse_qkv: false
flash_attn_fuse_mlp: true
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-4
warmup_steps: 100
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed: deepspeed_configs/zero1.json # multi-gpu only
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: <|end_of_text|>
Red_Llama_3_base
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8173
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=0.0001
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
1.1958 | 0.03 | 1 | 1.1846 |
1.0515 | 0.49 | 19 | 1.0706 |
0.952 | 0.99 | 38 | 0.9385 |
0.9038 | 1.44 | 57 | 0.8796 |
0.8679 | 1.94 | 76 | 0.8469 |
0.7675 | 2.39 | 95 | 0.8280 |
0.7643 | 2.88 | 114 | 0.8173 |
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0